In this chapter, the author presents an artificial intelligence (AI)-based generic concept for decision making using data science. The applied holistic mathematical model for AI (AHMM4AI) focuses on data and access management. A decisive business decision in a business transformation process of a traditional business environment into an automated AI-based business environment is the capacity of the decision-making system and the profile of the business transformation manager (BTM, or simply the manager). The manager and his team are supported by a holistic framework. The role of data science and the needed data modelling techniques are essential for managing data models in a transformation project. For that reason, the development of the big data for AI (BGD4AI) is an essential start.
TopIntroduction
The success of a Project depends on how an Enterprise Architecture for AI (EA4AI), data architecture and modelling activities are synchronized (IMD, 2015).
Figure 1. Framework’s cycles and the data access block
That is why the implementation of such Projects requires significant knowledge of data architecture, implementation and modelling techniques. The main GAIP mechanisms are: 1) generic data architecture; 2) implementation interfaces; and 3) data modelling is a part of the Selection management, Architecture-modelling, Control-monitoring, Decision-making, Training management and Project management Framework (SmAmCmDmTmPmF, for simplification in further text the term TRADf (that stands for the Transformation, Research, Architecture and Development framework) will be used), that supports the Project’s activities. As shown in Figure 1, the Data Sciences Integration for Artificial Intelligence (DSI4AI) interacts with all the enterprise’s (or simply an Entity) architecture phases, using the data Building Blocks for AI (dBB4AI) or the holistic brick (Trad & Kalpić, 2020a). The chapter is based on complex framework and it is just an extension to it and it includes many subdomains. This is the case of all cross functional and holistic ones. The DSI4AI explains, describes the basics of AI to support the GAIP based transformation project. Such projects are complex undertakings, which are based on the selection and DSI4AI based classification and weightings of the most important critical success factors and areas, which are used as global variables in the author’s specialized transformation framework. Where in this chapter the main subject is DSI4AI for GAIP’s integration. The GAIP for transformations projects. Such transformation projects can be applied to various Application and Problem Domains (APD), like finance, geopolitics, intelligent cities and other. Such APDs need a specific AI that is based on DSI4AI and AHMM4AI.
TopBackground
GAIP and DSI4AI are based on EA4AI, various AI fields, data architecture and modelling technologies. Where the DS4AI should include:
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A data foundational model, or a set of classes/entities, which can be integrated in various architectures; that use calls to various types of algorithms.
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The use of atomic Building Blocks for AI (aBB4AI) concept; which corresponds to an autonomous set of classes.
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The use of a Natural Programming Language for AI (NLP4AI) for development of data interfaces.
The author’s long years global research topic's and final Research Question (RQ) (hypothesis #1-1) is: “Which business transformation manager’s characteristics and which type of support should be assured for the implementation phase of a business transformation project?” The targeted business domain is any business environment that uses: 1) complex technologies; and 2) frequent transformation iterations. For this phase of research, the sub-question is: “What is the impact of the BGD4AI on EA4AI and Projects?”
TopMain Focus Of The Chapter
In this research phase the author’s target is BGI4AI’s that is a part of the Architecture module (Am), and he tries to prove that such a concept can be built on a loosely coupled architecture. The BGI4AI uses the Data Management Concepts for Artificial Intelligence (DMC4AI) to interface data sources. The DS4AI uses Mathematics for Data Science or the already presented AHMM4AI, which deals with mathematical models and algorithms that are used to analyse data, offers conclusions and it supports the DS4AI. Projects are increasingly digital and data is global; these huge amounts of data are full of valuable operational information. The BGI4AI supports data extraction in a way that to be used by AHMM4AI. In this chapter the AHMM4AI uses algorithms which are essential for analysing data and thus providing a basis for evidence-based decisions in various domains and in complex situations.